2 1750 S. T. BERRY AND P. A. HAILE choice demand model is combined with an oligopoly model of supply in order to estimate markups, predict equilibrium responses to policy, or test hypotheses about firm behavior. Typically, these models are estimated using econometric specifications incorporating functional form restrictions and parametric distributional assumptions. Such restrictions may be desirable in practice: estimation in finite samples always requires approximations and, since the early work of McFadden (1974), an extensive literature has developed providing flexible discrete choice models well suited to estimation and inference. Furthermore, parametric structure is necessary for the extrapolation involved in many out-of-sample predictions. However, an important question is whether these parametric functional form and distributional assumptions play a more fundamental role in determining what is learned from the data. In particular, are such assumptions essential for identification? Here, we examine the nonparametric identifiability of models in the spirit of Berry, Levinsohn, and Pakes (1995) (henceforth, BLP ) and a large applied literature that has followed. We focus on the common situation in which only market level data are available, as in BLP. In such a setting, one observes market shares, market characteristics, product prices and characteristics, and product/market level cost shifters, but not individual choices or firm costs. We consider identification of demand, identification of changes in aggregate consumer welfare, identification of marginal costs, identification of firms marginal cost functions, and discrimination between alternative models of firm conduct. We also provide guidance for applied work by focusing attention on the essential role of instrumental variables, clarifying the types of instruments needed in this setting, and pointing out tradeoffs between functional form and exclusion restrictions. Our primary motivation is to develop a nonparametric foundation for a class of empirical models used widely in practice. Nonetheless, our analysis may also suggest new estimation and/or testing approaches (parametric, semiparametric, or nonparametric). On the demand side, the models motivating our work incorporate two essential features. One is rich heterogeneity in preferences, which allows flexibility in demand substitution patterns. 3 The second is the presence of product/market level unobservables. Because these unobservables are known by firms and consumers, they give rise to endogeneity of prices. Only by explicitly modeling these unobservables can one account simultaneously for endogeneity and heterogeneity in preferences for product characteristics (see Section 2). Both features are essential to reliable estimation of demand elasticities in differentiated products markets. Surprisingly, this combination of features has not been treated in the prior literature on identification. Indeed, although there is a 3 See, for example, the discussions in Domencich and McFadden (1975), Hausman and Wise (1978), and Berry, Levinsohn, and Pakes (1995). Early models of discrete choice with heterogeneous tastes for characteristics include those in Quandt (1966, 1968).

3 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS 1751 large literature on identification of discrete choice models, there are no nonparametric or semiparametric identification results for even the linear random coefficients random utility model widely used in the applied literature that motivates us. On the supply side, the empirical literature on differentiated products employs equilibrium oligopoly models, building on early insights of Rosse (1970) and Bresnahan (1981). By combining the model of oligopoly competition with estimates of demand, one can infer marginal costs and examine a range of market counterfactuals. Following BLP, recent work typically allows for latent cost shocks and unobserved heterogeneity in cost functions, but employs a parametric specification of costs. Our results show that the primary requirement for identification is the availability of instrumental variables. It is not surprising that instruments are needed. Less obvious is what types of exclusion restrictions suffice in this setting. Focusing on the demand side, it is intuitive that identification requires instruments generating exogenous variation in choice sets, including changes in prices. For example, BLP exploited a combination of exogenous own-product characteristics, characteristics of alternative products, and additional shifters of markups and/or costs. Following Bresnahan (1981), BLP made an intuitive argument that changes in the exogenous characteristics of competing products should help to identify substitution patterns. Ignoring the other shifters of markups and costs, characteristics of competing products are sometimes referred to as BLP instruments. However, there has been no general formal statement about the role of BLP instruments why they would aid identification and whether they are sufficient alone to identify demand. One difficult question has been: how could product characteristics that are not excluded from the demand system help to identify demand? We show that the BLP instruments are in fact useful. For our formal results, they are necessary but not sufficient: we require additional instruments such as cost shifters or proxies for costs (e.g., prices in other markets). However, we also discuss tradeoffs between functional form and exclusion restrictions, including cases in which cost shifters alone or the BLP instruments alone could suffice. In Berry and Haile (2010), we considered identification of demand when one has consumer level choice data. There, we obtained results requiring fewer instruments, in some cases allowing identification when only exogenous product characteristics (the BLP instruments ) are available. In the following, we begin with demand, positing a nonparametric random utility discrete choice model. We require one important index restriction on how product/market-specific unobservables enter preferences, but the model is otherwise very general. Our first result shows that standard nonparametric instrumental variables conditions (completeness conditions discussed in, e.g., Newey and Powell (2003) or Andrews (2011)) suffice for identification of demand. As usual, adding a requirement of quasilinear preferences can then allow identification of changes in aggregate consumer welfare.

4 1752 S. T. BERRY AND P. A. HAILE We then move to the supply side of the model. Given identification of demand, specifying a model of oligopoly competition allows identification of marginal costs through firms first-order conditions. Identification of firms marginal cost functions then obtains with the addition of standard nonparametric instrumental variables conditions. Next, we drop the nonparametric instrumental variables conditions and consider an alternative approach, combining the demand model and supply model in a single system of nonparametric simultaneous equations. This approach requires some additional structure and stronger exclusion conditions, but enables us to offer constructive proofs using more transparent variation in excluded demand shifters and cost shifters. These results therefore complement those obtained using more abstract completeness conditions. Finally, we consider discrimination between alternative models of firm conduct, that is, alternative models of oligopoly competition. The nature of oligopoly competition is itself a fundamental question of modern industrial organization, and, in practice, the choice of supply model can have important implications for estimates, counterfactual simulations, and policy implications. We offer the first general formalization of Bresnahan s (1981, 1982) early intuition for empirically discriminating between alternative oligopoly solution concepts. Our results do not require rotations of demand. And, unlike prior formal results (Lau (1982)), ours allow product differentiation, heterogeneous firms, latent shocks to demand and costs, and oligopoly models outside the problematic conectural variations framework. Together, these results provide a positive message regarding the faith we may have in a large and growing body of applied work on differentiated products markets. This message is not without qualification: in addition to the index restrictions, our results require instruments with sufficient variation on both the demand and cost side. However, while adequate exogenous variation is a strong requirement for any nonparametric model, the requirement here is no stronger than for regression models. Put differently, the functional form and distributional assumptions typically used in practice play their usual roles: approximation in finite samples and compensation for the gap between the exogenous variation available in practice and that required to discriminate between all nonparametric models. To our knowledge, we provide the first and only results on the nonparametric identification of market level differentiated products models of the sort found in BLP and related applications in IO. However, our work is connected to several theoretical and applied literatures. In the following section, we briefly place our work in the context of the prior literature. We then set up the model in Section 3. Our analysis based on nonparametric instrumental variable conditions is presented in Section 4. Section 5 develops our alternative simultaneous equations approach. We take up discrimination between oligopoly models in Section 6. Section 7 provides a discussion focused on tradeoffs between functional form and exclusion restrictions. We conclude in Section 8.

5 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS RELATED LITERATURE There is a large body of work on the identification of discrete choice models. 4 Much of that literature considers models allowing for heterogeneous preferences through a random coefficients random utility specification, but ruling out endogeneity. Ichimura and Thompson (1998), for example, studied a linear random coefficients binary choice model. Briesch, Chintagunta, and Matzkin (2010) considered multinomial choice, allowing some generalization of a linear random coefficients model. In contrast to this literature, our formulation of the underlying random utility model specifies only random utilities, not random parameters that interact with observables to generate random utilities. This allows us to substantially relax functional form and distributional assumptionsreliedoninearlierwork. Also essential to our demand model is the endogeneity of prices. Several papers address the identification of discrete choice models with endogeneity. Examples include Lewbel (2000, 2006), Honoré and Lewbel (2002), Hong and Tamer (2003), Blundell and Powell (2004), and Magnac and Maurin (2007). These considered linear semiparametric models, allowing for a single additive scalar shock (analogous to the extreme value or normal shock in logit and probit models) that may be correlated with some observables. Among these, Lewbel (2000, 2006) considered multinomial choice. Extensions to nonadditive shocks were considered in Matzkin (2007a, 2007b). Compared to these papers, we relax some functional form restrictions and, more fundamentally, add the important distinction between market/choice-specific unobservables and individual heterogeneity in preferences. This distinction allows the model to define comparative statics that account for both heteroskedasticity (heterogeneity in tastes for product characteristics) and endogeneity. 5 For example, to define a demand elasticity, one must quantify the changes in market shares resulting from an exogenous change in price. Accounting for heterogeneity in consumers marginal rates of substitution between income and other characteristics requires allowing the price change to affect the covariance matrix (and other moments) of utilities. On the other hand, controlling for endogeneity requires holding fixed the market/choice-specific unobservables. Meeting both requirements is impossible in models with a single composite error for each product. Blundell and Powell (2004), Matzkin (2004), and Hoderlein (2009) have considered binary choice with endogeneity in semiparametric triangular models, leading to the applicability of control function methods or the related idea 4 Important early work includes Manski (1985, 1988) and Matzkin (1992, 1993), which examined semiparametric models with exogenous regressors. 5 Matzkin (2004) (Section 5.1) made a distinction between choice-specific unobservables and an additive preference shock, but in a model without random coefficients or other sources of heteroskedasticity/heterogeneous tastes for product characteristics. See also Matzkin (2007a, 2007b).

6 1754 S. T. BERRY AND P. A. HAILE of unobserved instruments (see also Petrin and Train (2010), Altoni and Matzkin (2005), and Gautier and Kitamura (2013)). 6 However, standard models of oligopoly pricing in differentiated products markets imply that each equilibrium price depends, in general, on the entire vector of demand shocks (and typically, the vector of cost shocks as well). This rules out a triangular structure and, therefore, a control function approach except under restrictive functional form restrictions (see Blundell and Matzkin (2010)). Nonetheless, some of our results use a related strategy of inverting a multiproduct supply and demand system to recover the entire vector of shocks to costs and demand. This could be interpreted as a generalization of the control function approach. It is also related to the small literature on nonparametric simultaneous equations. Indeed, we show that our demand and supply model can be transformed into a system of simultaneous equations with a general form first explored by Matzkin (2007a, 2008) (see also the discussion in Section 6). Here, we provide both a new result and an application of a result in Berry and Haile (2013). In the literature on oligopoly supply, Rosse (1970) introduced the idea of using first-order conditions for imperfectly competitive firms to infer marginal costs from prices and demand parameters. Although Rosse considered the case of monopoly, subsequent work extended the idea to models of simultaneous price-setting, quantity-setting, and to conectural variations models. In a simple case, this literature might consider an IV regression of price minus markup on a parametric form for marginal cost, including an additive unobserved cost shock. Bresnahan (1981, 1987) estimated the supply model using the reduced form for prices implied by the demand parameters and oligopoly pricing first-order conditions. BLP incorporated structural errors (demand shocks and cost shocks) and explicitly solved the multiproduct oligopoly pricing first-order conditions for the implied levels of marginal cost. Their approach is a parametric version of the nonparametric strategy we take. However, rather than focusing on a single model of supply, we exploit the connected substitutes property of demand (Berry, Gandhi, and Haile (2013)) in order to provide identification results applicable to a wide range of oligopoly supply models. Our insights regarding discrimination between alternative oligopoly models are related to ideas from the early empirical IO literature on inferring firm conduct from market outcomes. Bresnahan (1982), in particular, provided influential intuition for how rotations of demand could distinguish between alternative oligopoly models. While Bresnahan s intuition suggested wide applicability, formal results (Lau (1982)) have been limited to deterministic homogeneous goods conectural variations models (with linear conectures ), and have required exogenous variation that alters both the level and slope of aggregate demand (a point emphasized by Nevo (1998)). 7 Our results avoid 6 See also Chesher (2003) and Imbens and Newey (2009). 7 Bresnahan s (1982) intuitive insight concerns discrimination between pairs of oligopoly models. Lau (1982) focused on the particular implementation recommended by Bresnahan (1982,

7 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS 1755 these restrictions and point to a broader class of cross-market or cross-product variation leading to testable restrictions that can discriminate between alternative models. Turning to concurrent unpublished papers, Berry and Haile (2009) explored related ideas in the context of a generalized regression model (Han (1987)). Berry and Haile (2010) considered identification of discrete choice models in the case of micro data, where one observes the choices of individual consumers as well as characteristics specific to each consumer and product. The distinction between market data and micro data has been emphasized in the recent industrial organization literature (e.g., Berry, Levinsohn, and Pakes (2004)), but not the econometrics literature. A key insight in Berry and Haile (2010) is that, within a market, the market/choice-specific unobservables are held fixed. One can therefore learn key features of a random utility model by exploiting within-market variation variation that is not confounded by variation in the market/choice-specific unobservables. Such a strategy cannot be applied to market level data, but was exploited throughout Berry and Haile (2010). There, we show that this additional variation can reduce the need for exclusion restrictions, can make additional instruments available, and can allow one to drop the index structure relied on throughout the present paper. Matzkin (2010) considered estimation of simultaneous equations models of the form studied by Matzkin (2008) and included a pair of new identification results that could be extended to that form. Fox and Gandhi (2009) explored the identifiability of a discrete choice model in which consumer types are multinomial and conditional indirect utility functions are analytic. 8 Fox and Gandhi (2011) explored an extension in which the dimension of the product/market level unobservables exceeds that of the choice set, showing that ex ante average features of demand can still be identified. Chiappori and Komuner (2009) combined a simultaneous equations approach with completeness conditions to consider identification using micro data (cf. Berry and Haile (2010)) in a random utility discrete choice demand model with an additive structure. 3. DEMAND MODEL 3.1. Random Utility Discrete Choice Each consumer i in market t chooses a good from a set J t ={0 1 J t }. We use the terms good and product interchangeably. A market consists 1989), which involves estimation of conectural variations parameters (often referred to as conduct parameters ). Corts (1999) and Reiss and Wolak (2007) discussed fundamental problems with the conduct parameter approach. 8 Baari, Fox, Kim, and Ryan (2012) considered identification in a linear random coefficients model without endogeneity, assuming that the distribution of an additive i.i.d. preference shock is known. Gandhi, Kim, and Petrin (2011) considered identification and estimation of a particular parametric variation on the standard BLP model.

8 1756 S. T. BERRY AND P. A. HAILE of a continuum of consumers (with total measure M t ) in the same choice environment. In practice, markets are often defined geographically and/or temporally; however, other notions are permitted. For example, residents of the same city with different incomes, races, or family sizes might be split into distinct markets. Formally, a market t is defined by (J t χ t ),where χ t = (x t p t ξ t ) (with support X ) represents the characteristics of products and/or markets. Observed exogenous characteristics of the products or market are represented by x t = (x 1t x Jt t), witheachx t R K. The vector ξ t = (ξ 1t ξ Jt t), with each ξ t R, represents unobservables at the level of the product and/or market. These may reflect unobserved product characteristics and/or unobserved variation in tastes across markets. Although a scalar unobservable for each product >0 is standard in the literature, it is an important restriction that will constrain the ways in which the distribution of utilities can vary across markets. On the other hand, a need to have no more than one structural error per observed choice probability may not be surprising. Finally, p t = (p 1t p Jt t), witheachp t R, represents endogenous observable characteristics, that is, those correlated with the structural errors ξ t. The restriction to one scalar endogenous observable for each good/market reflects standard practice but is not essential. 9 We refer to p t as price, reflecting the leading case. Without loss of generality, we henceforth condition on J t = J ={0 1 J}. 10 Consumer preferences are represented with a random utility model. 11 Consumer i in market t has conditional indirect utilities v i0t v i1t v ijt for the goods. For simplicity, we will refer to these as utilities. Without loss, we normalize utilities relative to that of good 0 for each consumer, implying v i0t = 0 i t Any observed characteristics of good 0 in market t are then treated as marketspecific characteristics common to all products >0 in that market. In applications, it is common for good 0 to represent the composite outside good, 9 The modifications required to allow higher dimensional p t are straightforward, although the usual challenge of finding adequate instruments would remain. See the discussion in Appendix C, which also demonstrates that, in some special cases, additional instruments will not be necessary. For an empirical analysis with multiple endogenous characteristics per product, see Fan (2013). 10 We do not impose any restriction linking distributions of preferences across markets with different values of J t. An interesting question is whether imposing such restrictions would allow weakening of our identification conditions. 11 See, for example, Block and Marschak (1960), Marschak (1960), McFadden (1974), and Manski (1977) for pioneering work.

9 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS 1757 that is, the decision to purchase none of the goods explicitly studied, and this outside good may have no observable characteristics. 12 Our formulation allows this as well. Conditional on χ t, the utilities (v i1t v ijt ) are independent and identically distributed (i.i.d.) across consumers and markets, with oint distribution F v (v i1t v ijt χ t ) We assume arg max J v it is unique with probability 1. Choice probabilities (market shares) are then given by ( (1) s t = σ (χ t ) = Pr arg max v ikt = χ t ) = 0 J k J Of course, σ 0 (χ t ) = 1 J =1 σ (χ t ). We assume that, for all χ t X, σ (χ t )>0 for all J. 13 Let s t = (s 1t s Jt ) and σ(χ t ) = (σ 1 (χ t ) σ J (χ t )) An Index Restriction So far, the only restriction placed on the random utility model is the restriction to a scalar product/market unobservable ξ t for each t and = 1 J. We now add an important index restriction. Partition x t as (x (1) t x (2) t ), with x (1) t R. Letx (1) t = (x (1) 1t x(1) Jt ) and x (2) t = (x (2) 1t x(2) Jt ). Define the linear indices (2) δ t = x (1) t β + ξ t and let δ t = (δ 1t δ Jt ). = 1 J ASSUMPTION 1: F v ( χ t ) = F v ( δ t x (2) t p t ). Assumption 1 requires that ξ t and x (1) t affect the distribution of utilities only through the indices δ t. With this assumption, variation in p t and x (2) t can have arbitrary effects on the way variation in ξ t changes the distribution of v it,but x (1) t and ξ t are perfect substitutes. Put differently, the marginal rate of substitution between a unit of the characteristic measured by x t and that measured by ξ t must be constant. This linear structure is actually stronger than necessary, 12 We have implicitly assumed that any unobservable specific to good 0 is held fixed. In the special case in which the unobservables ξ t enter utilities linearly, a nonconstant unobservable ξ 0t could be accommodated simply by redefining the structural errors as ξ t = ξ t ξ 0t >0. In Berry, Gandhi, and Haile (2013) we showed that, outside the case of single discrete choice, one can use an artificial notion of good zero that allows stochastic unobservables for every real good. This yields a demand system with the same structure studied here, but where good zero is only a technical device rather than one of the available options. 13 It should be clear that at most a bound could be obtained on the distribution of utility for a good with zero market share.

10 1758 S. T. BERRY AND P. A. HAILE and we show in Appendix B that identification with a nonparametric IV approach can also be obtained with nonseparable indices δ t = δ (x (1) t ξ t ).The essential requirement is that x (1) t and ξ t enter through an index that is strictly monotonic in ξ t. EXAMPLE 1: In applied work, it is common to generate the conditional oint distributions F v ( χ t ) from an analytical form for random utility functions v it = v (x t ξ t p t ; θ it ) where θ it is a finite-dimensional parameter. The most common form is a linear random coefficients model, for example, (3) v it = x t β it α it p t + ξ t + ɛ it with θ it = (α it β it ɛ i1t ɛ ijt ) independent of χ t and i.i.d. across consumers and markets. Endogeneity of price is reflected by correlation between p t and ξ t conditional on x t. The specification (3) generalizes the BLP model by dropping its parametric distributional assumptions and allowing correlation among the components of θ it. 14 In this model, a sufficient condition for Assumption 1 is that one component of x t have a degenerate coefficient, an assumption made in most applications. Compared to this example, our model relaxes several restrictions. For example, we do not require the linearity of (3), finitedimensional θ it, or even the exclusion of (x kt ξ kt p kt ) from v ( ) for k. Because the unobservables {ξ 1t ξ Jt } have no natural location or scale, we must normalize them in order to have a unique representation of preferences. 15 We normalize the scale by setting β = 1, yielding δ t = x (1) t + ξ t This leaves a location normalization on each ξ t to be made later. Henceforth, we condition on an arbitrary value of x (2) t and suppress it. Although this provides considerable simplification of notation, we emphasize that the distribution of utilities can vary in a quite general way with x (2) t.forsimplicity, we then let x t represent x (1) t and let χ t (with support X ) now represent p t ξ t ). Given Assumption 1, we will often abuse notation and write (x (1) t σ(δ t p t ) instead of σ(χ t ) for the sake of clarity. 14 In the BLP model, price enters through a nonlinear interaction with random coefficients. Since the distinction has no substantive implication for our purposes, we ignore this and refer to the model with linear interactions as the BLP model. 15 For example, let ξ t = α + ξ t β and F v ( x t p t ξ t ) = F v ( x t p t ξ t ) (x t p t ξ t ). Then, for every (α β) R J R J +, we have a different representation of the same preferences.

11 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS Connected Substitutes Standard discrete choice models inherently involve choice among weak gross substitutes. 16 For example, if v it is strictly decreasing in its price p t,afallin p t will (all else equal) raise the market share of good and (weakly) lower market shares of all other goods. With the following connected substitutes assumption (Berry, Gandhi, and Haile (2013)), we will strengthen this notion of choice among substitute goods in two ways. First, we require that the index δ t also act like (minus) price in the sense that a rise in δ t will (all else equal) weakly lower the market shares of all goods k. Second, we will require a minimal degree of strict substitution among the goods. DEFINITION 1: Let λ t denote either p t or δ t. Goods (0 1 J) are connected substitutes in λ t if both (i) σ k (δ t p t ) is nonincreasing in λ t for all >0, k, (δ t p t ) R 2J ;and (ii) at each (δ t p t ) supp(δ t p t ), for any nonempty K {1 J} there exist k K and l/ K such that σ l (δ t p t ) is strictly decreasing in λ kt. ASSUMPTION 2: Goods (0 1 J) are connected substitutes in p t in δ t. and Following the definition, Assumption 2 has two parts. Part (i) requires the goods to be weak gross substitutes in p t and in δ t. Part (ii) requires some strict substitution as well loosely speaking, enough to ustify treating J as the relevant choice set. In particular, if part (ii) failed, there would be some strict subset K of goods that substitute only to other goods in K. A sufficient condition for Assumption 2 is that each v it be strictly increasing in δ t and p t,but unaffected by (δ kt p kt ) for k. Given the index restriction, this would be standard. But this is stronger than necessary; for example, while Assumption 2 implies that the aggregate choice probability σ (δ t p t ) is strictly increasing in δ t (see Berry, Gandhi, and Haile (2013)), it still allows an increase in δ t to lower v it for a positive measure of consumers. Thus, x it and ξ t need not be vertical characteristics. 17 Berry, Gandhi, and Haile (2013) provided additional discussion of the connected substitutes property and demonstrated that it captures a feature common among standard models of differentiated products demand. This will be a key condition allowing us to invert our supply and demand models. 16 Complements can be accommodated by defining the products as bundles of the individual goods (e.g., Gentzkow (2007)). 17 For example, x (1) t and ξ t could be observed and unobserved factors contributing to a horizontal characteristic say, the acceleration capacity of a car which consumers as a whole like, but which some consumers dislike. This is an additional way in which our setup generalizes the standard linear random coefficients model of Example 1.

13 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS 1761 are endogenous and φ( ) is unknown. Newey and Powell (2003) showed identification of φ( ) using excluded instrumental variables with dimension at least equal to that of ỹ (2) t. In similar notation, our inverted demand model (6) takes the form φ (ỹ (1) t ỹ (2) t ) x t = ũ t for each. Here, all endogenous variables appear inside the unknown transformation φ, and an essential instrument, x t, is not excluded. This precludes direct application of the Newey Powell result. However, we will see that following their identification proof will allow us to demonstrate identification in our setting, using as instruments the exogenous x t R J in combination with excluded instruments of dimension at least equal to that of ỹ (2) t. Before stating the instrumental variables conditions, recall that, in addition to the exogenous product characteristics x t, the vector z t represents instruments for p t excluded from the determinants of (v i1t v ijt ). Standard excluded instruments include cost shifters (e.g., input prices) or proxies for cost shifters such as prices of the same good in other markets (e.g., Hausman (1996), Nevo (2001)). In some demand applications, characteristics of consumers in nearby markets will be appropriate instruments (e.g., Gentzkow and Shapiro (2010), Fan (2013)). We make the following exclusion and completeness assumptions. ASSUMPTION 3: For all = 1 J, E[ξ t z t x t ]=0 almost surely. ASSUMPTION 4: For all functions B(s t p t ) with finite expectation, if E[B(s t p t ) z t x t ]=0 almost surely, then B(s t p t ) = 0 almost surely. Assumption 3 is a standard exclusion restriction, requiring mean independence between the instruments and the structural errors ξ t. 18 Assumption 4 requires completeness of the oint distribution of (z t x t s t p t ) with respect to (s t p t ). 19 This is a nonparametric analog of the standard rank condition for linear models. In particular, it requires that the instruments move the endogenous variables (s t p t ) sufficiently to ensure that any function of these variables can be distinguished from others through the exogenous variation in the instruments. See Newey and Powell (2003), Severini and Tripathi (2006), Andrews (2011), and references therein for helpful discussion and examples. We emphasize that we require both the excluded instruments z t and the exogenous 18 Observe that we do not require any restriction on the oint distribution of x (2) t and ξ t.thus our description of (x (1) t x (2) t ) as observed exogenous characteristics, while consistent with standard practice, suggests less flexibility than what is actually permitted. For example, if ξ t and x (2) t are ointly determined, this presents no problem for the identification of price elasticities as long as x (1) t is independent of ξ t conditional on x (2) t. We provide an example in Appendix C. 19 Identification with weaker forms of completeness follow from the same argument used below. L 2 -completeness (Andrews (2011)) would suffice under the mild restriction that the sum x t + ξ t has finite variance for all.ifξ t and x t were assumed to have bounded support for all, bounded completeness would suffice.

15 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS Identification of Changes in Consumer Welfare The demand system σ(χ t ) is the only primitive from the consumer model required for most purposes motivating estimation in differentiated products markets. The important exception is evaluation of changes in consumer welfare. It should be clear that, in general, changes in individual consumer welfare are not identified from market level data. 20 However, as usual, an assumption of quasilinear preferences will allow one to define a valid notion of aggregate consumer welfare (consumer surplus) in terms of aggregate demand (see, e.g., McFadden (1981), Small and Rosen (1981)). ASSUMPTION 5: v it = μ it p t, where the random variables (μ i1t μ ijt ) are independent of p t conditional on (x t ξ t ). We use this assumption only in this section of the paper. Note that the restriction v it = μ it p t would have no content alone: because p t χ t,any oint distribution F v ( χ t ) could be replicated by appropriate specification of the distribution F μ ( χ t ) of (μ i1t μ ijt ) conditional on χ t. The conditional independence adds the requirement that, fixing (x t ξ t ), prices affect the oint distribution of utilities only through the linear terms. Thus, Assumption 5 is a natural notion of quasilinearity for a random utility setting. 21 If one specified v it = v (x t ξ t ; θ it ) p t,whereθ it is a possibly infinite-dimensional random parameter (Example 1 is a special case), Assumption 5 holds when θ it p t conditional on (x t ξ t ); that assumption is standard in the literature. 22 We emphasize that the conditional independence assumption does not imply an absence of endogeneity: arbitrary dependence between p t and (x t ξ t ) is still permitted. Note that, under Assumptions 1 and 5, we can replace F μ ( χ t ) with F μ ( δ t ). The most common welfare questions in applications concern the effects of price changes, for example, those resulting from a merger, a tax, or a change in 20 Knowledge of the conditional distributions F v ( χ t ) would not imply identification of such welfare changes, since a given change in the distribution does not reveal the change in any individual s welfare. Many parametric models enable identification of changes in individual welfare (e.g., compensating/equivalent variation), even with market level data, by specifying each consumer (or each set of identical consumers) as associated with a particular realization of random parameters that completely determine her utilities (see Example 1). 21 Under the seemingly more general specification v it = μ it α it p t, with the random coefficient α it > 0 a.s., one can normalize the scale of each consumer s utilities without loss by setting α it = In industrial organization, see, for example, Berry, Levinsohn, and Pakes (1995) and the extensive applied literature that follows. In the econometrics literature, see, for example, Ichimura and Thompson (1998), Briesch, Chintagunta, and Matzkin (2010), or Gautier and Kitamura (2013). Exceptions are models in which no distinction is made between latent heterogeneity in preferences (here, θ it ) and unobserved characteristics of goods (here, ξ t ). As discussed in Section 2, such models have severe limitations in applications to demand.

16 1764 S. T. BERRY AND P. A. HAILE the form of competition. If F μ ( δ t ) admits a oint density f μ ( δ t ), the change in consumer surplus when prices change from p 0 to p 1 is given, as usual, by a line integral b σ(δ a t α(τ)) α (τ) dτ,whereα is a piecewise smooth path from α(a) = p 0 to α(b) = p 1. Thus, identification of demand on X immediately implies identification of the aggregate welfare effects associated with any pair of price vectors (p 0 p 1 ) lying in a path-connected subset of supp p t δ t. Whether the welfare effects of other types of changes in the environment are identified depends on the support of prices. Just as in classical consumer theory, evaluating the welfare effects of a change in the characteristics of goods requires sufficient exogenous price variation to allow integration under the entire left tail of demand (see, e.g., Small and Rosen (1981)). We can also see this by observing that, under Assumption 5, F v ( χ t ) determines the distribution of consumers money-metric indirect utility functions and can be used to directly calculate aggregate consumer surplus. Under Assumptions 1 and 5, F v ( χ t ) is completely determined by F μ ( δ t ) and the given level of prices. Thus, while not necessary for identification of all changes in consumer surplus, identification of F μ ( δ t ) for all δ t is sufficient. Under Assumption 5, analysis of point identification of F μ ( δ t ) is standard. With the notational convention p 0t = 0, let ṗ t denote the vector of price differences (p kt p t ) k J \ and let Γ denote the matrix such that Γ p t = ṗ t (see, e.g., Thompson (1989)). Let F denote the set of all cumulative distribution functions on R J. Under Assumptions 1 and 5, F μ ( δ t ) is an element of F satisfying (7) (8) σ 0 (δ t p t ) = F μ (p t δ t ) and σ (δ t p t ) = 1{Γ m Γ p t } df μ (m δ t ) = 1 J on the support of p t δ t. In general, given identification of σ(χ t ) on X, (7) and (8) deliver partial identification of F μ ( δ t ) for each δ t. However, under the large support condition supp p t δ t supp μ t δ t equation (7) directly provides point identification. Furthermore, in this case the restrictions in (8) are redundant to (7) (see, e.g., Thompson (1989)). With more limited support, what is learned about F μ ( δ t ) combines information obtained from each function σ (δ t p t ). Figure 1 illustrates for J = 2 (cf. Thompson (1989)). Given a price vector p t, panel (a) divides the space of μ it into regions leading to each possible consumer choice. Panel (b) illustrates how the boundaries between regions adust with prices. For example, a fall in the price of good 1 leads consumers with μ it in the horizontally hatched region to switch to good 1. With each σ (δ t p t ) identified, the measure of consumers in each region is known. This provides not only point identification of F μ ( δ t )

17 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS 1765 (a) (b) FIGURE 1. (a) Choice regions with J = 2. (b) Price changes move choice regions. on the support of p t δ t (from (7)), but also restrictions on the probability measure associated with strips of R 2 extending outside the support. For example, if F μ ( δ t ) admits a density and prices have support on an open set containing both p 1 and p 2, (7) and (8) imply p 2 f μ(p 1 μ 2 δ t )dμ 2 = σ 0 (δ t p)/ p 1, p1 f μ(μ 1 p 2 δ t )dμ 1 = σ 0 (δ t p)/ p 2, f p2 μ(μ 2 p 2 + p 1 μ 2 δ t )dμ 2 = σ 2 (δ t p)/ p 1, and f μ (p 1 p 2 δ t ) = 2 σ 0 (δ t p)/ p 1 p 2. These expressions demonstrate identification of the integral of f μ ( δ t ) over regions corresponding to the limits of those illustrated in panel (b) (in the last case, the crosshatched region) as the price changes shrink to zero Identification of Marginal Costs In the applied literature, adding a supply side to the model typically proceeds by specifying a functional form for firm costs and an extensive form for the competition between firms. A set of first-order conditions characterizing equilibrium prices and quantities in terms of firms costs and features of demand (e.g., own- and cross-price elasticities) then allow recovery of firms equilibrium marginal costs and, under additional exclusion restrictions, their marginal cost functions. Given identification of demand, it is straightforward to show that a similar approach can be applied to demonstrate identification in a nonparametric setting Although the results below will reference the particular demand model and sufficient conditions for identification of demand developed above, this is not essential. What is required in this section is that the demand elasticities be known in every market and that the goods be connected substitutes in prices. The same is true of the results in Appendix A and the result on testability

18 1766 S. T. BERRY AND P. A. HAILE An identification approach based on first-order conditions requires differentiability of firms profit functions with respect to their choice variables. This will be assured if the market share functions σ (χ t ) are continuously differentiable with respect to prices, and we will assume this directly. We also slightly strengthen part of the connected substitutes assumption (Assumption 2) by requiring σ k (χ t )/ p > 0 whenever σ k (χ t ) is strictly increasing in p. ASSUMPTION 6: (i) σ (χ t ) is continuously differentiable with respect to p k k J ; (ii) at each χ t X, for any nonempty K {1 J}, there exist k K and l/ K such that σ l (χ t )/ p kt > 0. Let mc t denote the marginal cost of production of good in market t given the observed levels of output (q 1t q Jt ). For now, we place no restriction on the structure of firm costs. However, we follow the parametric literature in assuming one has committed to a model of supply that implies a system of firstorder conditions defining firm behavior. In the typical model, each first-order condition can be solved to express mc t as a known function of equilibrium prices, equilibrium quantities (determined by s t and M t ), and the first-order derivatives of the demand system σ. 24 We let D(χ t ) denote the J J matrix of partial derivatives [ σ k(χ t ) p l ] k l. ASSUMPTION 7a: For each = 1 J, there exists a known function ψ such that, for all (M t χ t s t ) in their support, (9) mc t = ψ ( st M t D(χ t ) p t ) Although Assumption 7a is a high-level condition, the following remark (proved in Appendix A) demonstrates that, given connected substitutes in prices and Assumption 6, Assumption 7a follows from the first-order conditions characterizing equilibrium in the standard oligopoly supply models typically considered in the empirical literature, where ψ might be interpreted as a generalized residual marginal revenue function for product. Relying on this high-level assumption thus allows us to provide results for a variety of supply models at once. REMARK 1: Suppose that the goods {0 1 J} are connected substitutes in p t and that supply is characterized by any of the following models, each allowing single-product or multi-product firms: (i) marginal cost pricing; of the supply model in Theorem 9 below. See Berry, Gandhi, and Haile (2013) for examples of other demand structures satisfying the connected substitutes condition. 24 This does not require that the first-order conditions have a unique solution for prices or quantities.

19 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS 1767 (ii) monopoly pricing (or oint profit maximization); (iii) Nash equilibrium in a complete information simultaneous price-setting game; (iv) Nash equilibrium in a complete information simultaneous quantitysetting game. Then under Assumption 6, Assumption 7a holds. This remark demonstrates that Assumption 7a describes a feature common to standard models of oligopoly supply. This feature allows identification of marginal costs. In particular, the following result follows immediately from (9) and Theorem 1, since the latter implies identification of D(χ t ). THEOREM 2: Suppose Assumptions 1 4, 6, and 7a hold. Then mc t is identified for all t = 1 J. Theorem 2 shows that marginal costs (and, therefore, markups) are identified without any additional exclusion condition or any restriction on firms cost functions. Given any of the standard models of oligopoly supply described above, only the differentiability assumption (Assumption 6) has been added to the conditions we required for identification of demand. Marginal costs and markups are sometimes the only obects of interest on the supply side. However, unless one assumes constant marginal costs, these will not be sufficient for counterfactual questions involving changes in equilibrium quantities. We therefore move now to consider identification of firms marginal cost functions. Let J denote the set of goods produced by the firm producing good. Let q t = M t s t denote the quantity of good produced, and let Q t denote the vector of quantities of all goods k J.Wealloweachmarginalcostmc t to depend on all quantities Q t, on observable cost shifters w t (which may include or consist entirely of demand shifters), and on an unobserved cost shifter ω t R. Thus, we let (10) mc t = c (Q t w t ω t ) where the unknown function c may differ arbitrarily across goods. By Theorem 2, each mc t can be treated as known, so (10) takes the form of a standard nonparametric regression equation. We have a standard endogeneity problem: firm output Q t is correlated with the marginal cost shock ω t. However, typically there are many available instruments. Any demand shifter excluded from the cost shifters w t is a candidate instrument. These can include observable demand shifters x kt for k J. Furthermore, because standard oligopoly models imply that demand shifters for all goods affect every market share through the consumer choice problem and equilibrium, demand shifters x kt for k/ J are also available as instruments. Other possible

20 1768 S. T. BERRY AND P. A. HAILE instruments include observables like population that vary only at the market level and may or may not directly affect market shares, but do affect quantities. Thus, there will often be a large number of instruments for Q t. 25 Indeed, the large number of instruments may lead to testable overidentifying restrictions, something we discuss in Section 6. With adequate instruments, identification of the marginal cost function is straightforward. One way to obtain a formal result is to assume monotonicity of c in the unobserved cost shifter ω t and apply the identification result of Chernozhukov and Hansen (2005) for nonparametric nonseparable instrumental variables regression models. Alternatively, one could consider the separable nonparametric specification (11) mc t = c (Q t w t ) + ω t in which case identification of the unknown function c follows by direct application of Newey and Powell s (2003) identification result for separable nonparametric regression models. 26 We give a formal statement only for the latter case. Let y t denote instruments excluded from the own-product cost shifters, as discussed above, and make the following exclusion and completeness assumptions. ASSUMPTION 8: E[ω t w t y t ]=0 almost surely for all = 1 J. ASSUMPTION 9: For all = 1 J and all functions B(Q t w t ) with finite expectation, if E[B(Q t w t ) w t y t ]=0 almost surely, then B(Q t w t ) = 0 almost surely. THEOREM 3: Suppose marginal costs take the form in (11) and that Assumptions 1 4, 6, 7a, 8, and 9 hold. Then for all = 1 J,(i)the marginal cost functions c (Q t w t ) are identified, and (ii) ω t is identified with probability 1 for all t. PROOF: Immediate from Theorem 2 and Newey and Powell (2003). Q.E.D Identifying Cost Shocks Without a Supply Model We conclude our exploration of nonparametric instrumental variables approaches to identification by providing conditions under which the latent cost shocks ω t can be identified without specifying a particular oligopoly 25 In practice, it is usually assumed that only q t,notq t, enters the cost of good.inthatcase, only a single excluded instrument would be required. 26 Unlike our prior use of a Newey Powell inspired argument to identify demand, there is nothing nonstandard about equation (11): the left-hand-side variable is endogenous and all instruments are excluded from the equation.

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